Field of the Invention
The present invention relates to a method for quantifying classification confidence of objects, particularly to a method for quantifying classification confidence of obstructions applied to a perception mergence system.
Description of the Related Art
Presently, functions of vehicular computers become more and more perfect. In order to improve driving safety and consider the future of autonomous driving, the detection of obstructions in front of a vehicle and the confidence of classifying the obstructions are of importance. The obstructions are classified into different classifications, including vehicles, pedestrians, bicycles and utility poles. According to the system setting, classification items are decided. In this way, the system provides braking suggestion, automatically brakes quickly, or performs other activities according to the obstructions' classification.
FIG. 1 is a block diagram showing detection for obstructions in front of a vehicle and perception mergence in the traditional technology. A camera 10 retrieves a front road image, and a plurality of sensors 11 and 12 detect distances between themselves and front obstructions, or retrieve a vehicle body signal of the vehicle. The heights and profiles of the obstructions are obtained from the distance detected by the sensors 11 and 12. Then, the front road image, the obstruction information and the vehicle body signal are used to respectively analyzing the obstruction information 13 and to calculate mergence information for positions and classification 15 of the obstructions. Besides, the front road image, the obstruction information and the vehicle body signal are used to respectively estimating existence confidences 14, namely precision that the front obstructions exist. Also, the existence confidences merged 16. Finally, outputting information 17, and the information includes the probabilities that the obstructions indeed exist, coordinates and possible classification of the obstructions. However, there is no mechanism to determine whether the existence confidences calculated by the system are correct again. Thus, the mergence results of the existence confidences are directly trusted. The existence confidences directly trusted will lead to serious results if misjudgment occurs, Take a real case for example. As shown in FIG. 2, a vehicle 18a is provided with a vehicular computer having a system for front obstruction detection and classification warning. The vehicle 18a is a safe distance from a front vehicle 18b. A large tanker 18c drives on a right lane. When the tanker 18c passes by the vehicle 18a, the microwaves reflected from the front vehicle 18b are diffused by the tanker 18c. The system of the vehicle 18a receives the microwaves diffused to determine that the probability of hitting a front vehicle is very high, and then automatically brakes quickly. As a result, a rear vehicle 18d hits the vehicle 18a before reacting. In fact, there is no vehicle driving near the vehicle 18a and in front of the vehicle 18a. Instead, the tanker 18c drives on the right lane neighboring the vehicle 18a. The system misjudges that the noise represents a vehicle, which results in incorrect brake.
Accordingly, how to improve the precision of existence confidences and classification confidences and the reference for quantifying the mergence information to avoid misjudgment is an important problem. The present invention provides a method for quantifying classification confidence of obstructions and describes specific architectures and embodiments as followings: